ViSkill: Reinforcing VLM Agents with Evolving Visual-Native Skills
cs.CV, cs.CL
Submitted: 2026-10-08
Updated: 2026-10-08
Code: https://github.com/ZJU-REAL/ViSkill
Terminology
Sources
- Qwen2.5-VL Technical Report
- DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning
- Skill-CMIB: Multimodal Agent Skill for Consistent Action via Conditional Multimodal Information Bottleneck
- GPT-4o System Card
- XSkill: Continual Learning from Experience and Skills in Multimodal Agents
- Perceive-to-Reason: Decoupling Perception and Reasoning for Fine-Grained Visual Reasoning
- MUSE-Autoskill: Self-Evolving Agents via Skill Creation, Memory, Management, and Evaluation
- SkillLens: Visual Skill Cards for Retrieval-Augmented GUI Action Prediction and On-Policy Distillation
- Visual Agentic Reinforcement Fine-Tuning
- SKILL0: In-Context Agentic Reinforcement Learning for Skill Internalization
- ManiSkill: Generalizable Manipulation Skill Benchmark with Large-Scale Demonstrations
- Trace2Skill: Distill Trajectory-Local Lessons into Transferable Agent Skills
- DINOv2: Learning Robust Visual Features without Supervision
- Proximal Policy Optimization Algorithms
- VLM-R1: A Stable and Generalizable R1-style Large Vision-Language Model
- Skill1: Unified Evolution of Skill-Augmented Agents via Reinforcement Learning
- Voyager: An Open-Ended Embodied Agent with Large Language Models
- AtlasVA: Self-Evolving Visual Skill Memory for Teacher-Free VLM Agents
- RAGEN: Understanding Self-Evolution in LLM Agents via Multi-Turn Reinforcement Learning
- Milestone-Guided Policy Learning for Long-Horizon Language Agents
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